Beijing Zhong Ke San Huan Hi-tech Co., Ltd.

Sanvac (beijing) Magnetics Co., Ltd.

Tsinghua University

Bin Fang

Yugang Zhao

Guoan Chen

Huaping Liu

Haojie Wang

Fuchun Sun

Zhan Wang

Fei Du

Libai Sun

The present invention proposes a fabric defect detection method based on multi-modal deep learning. First, a tactile sensor is placed onto the fabric surface with different defects to collect the fabric texture images, a camera is used to collect the corresponding fabric external images, and a fabric external image and a fabric texture image constitute a set of fabric detection data; then, a feature extraction network and a multi-modal fusion network are connected to establish a classification model based on multi-modal deep learning, which uses the fabric texture image and fabric external image in each set of collected fabric detection data as input, and the fabric defect as output; said classification model is trained using the collected fabric detection data; finally, the trained classification model is used to detect the fabric defect. The present invention employs vision-touch complementary information, which can greatly improve the accuracy and robustness of detection.

Fabric defect detection method based on multi-modal deep learning

A NdFeB rare earth magnet includes a main phase and a grain boundary phase including a white grain boundary phase and a gray grain boundary phase. In a microstructure observation area of the rare earth magnet, an area of the white grain accounts for 1˜3% of a total area of the microstructure observation area, and an area of the gray grain boundary phase accounts for 2˜10% of the total area of the microstructure observation area.

Rare earth magnet and preparation method thereof

The invention proposes an automatic throat swab sampling system, which comprises an automatic to-be-tested person identity information checking and collection prompting unit, a to-be-tested person head positioning unit, a navigation positioning unit which enters the oral cavity of a to-be-detected person along with the tail end of the sample collection execution unit for collecting a throat image and determining depth information between a depth sensor and the throat of the to-be-detected person, a sample collection execution unit including a three-degree-of-freedom guide rail type device and a one-degree-of-freedom end execution mechanism, an automatic throat swab loading and unloading unit and a remote monitoring unit which are arranged on a working platform. In a throat swab collecting process, the real-time information of all units communicates with the remote monitoring unit, and medical staff monitors the collected person and all the units in real time through a computer in a remote safety room, so that measures can be taken timely to guarantee the safety and effectiveness of the whole process when an accident occurs. The system realizes the full-automatic throat swab collection process.

Automatic throat swab sampling system

A composite coating layer for coating a NdFeB rare earth magnet includes a first coating layer and a second coating layer formed over a surface of the first coating layer. The first coating layer includes a Nd coating layer, a Pr coating layer, or an alloy coating layer including two or more of Nd, Pr, and Cu. The second coating layer includes a Tb coating layer.

Coating machine and coating method

The current invention discloses a type of micronized powder for manufacturing sintered Neodymium magnetic material, a target type jet mill pulverization method to prepare the micronized powder, and the resulting pulverized powder. The Neodymium magnet powder created under the method is of sphericity of greater than or equal to 90% and of particle adhesion rate of less than or equal to 10%. A is the diameter of the target center, B is the diameter of the side nozzle, and C is the distance between the target center and the nozzle. The relationship amongst A, B and C is A/B=m&#xd7;(C/A+B), where m ranges from 1 to 7. A velocity of the jet stream from side nozzle is between about 320 m/s to about 580 m/s.

Growing community of inventors

Beijing, China

Bin Fang